Text classification is an important problem with many applications. Traditional approaches represent text as a bag-of-words and build classifiers based on this representation. Rather than words, entity phrases, the relations between the entities, as well as the types of the entities and relations carry much more information to represent the texts. This paper presents a novel text as network classification framework, which introduces 1) a structured and typed heterogeneous information networks (HINs) representation of texts, and 2) a meta-path based approach to link texts. We show that with the new representation and links of texts, the structured and typed information of entities and relations can be incorporated into kernels. Particularly, we develop both simple linear kernel and indefinite kernel based on meta-paths in the HIN representation of texts, where we call them HIN-kernels. Using Freebase, a well-known world knowledge base, to construct HIN for texts, our experiments on two benchmark datasets show that the indefinite HIN kernel based on weighted meta-paths outperforms the state-of-the-art methods and other HIN-kernels.

With the recent growth of online content on the Web, there have been more user generated data with noisy and missing labels, e.g., social tags and voted labels from Amazon's Mechanical Turks. Most of machine learning methods, which require accurate label sets, could not be trusted when the label sets were yet unreliable. In this paper, we provide a text label refinement algorithm to adjust the labels for such noisy and missing labeled datasets. We assume that the labeled sets can be refined based on the labels with certain confidence, and the similarity between data being consistent with the labels. We propose a label smoothness ratio criterion to measure the smoothness of the labels and the consistency between labels and data. We demonstrate the effectiveness of the label refining algorithm on eight labeled document datasets, and validate that the results are useful for generating better labels.